


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 SCREAMING_SNAKE_CASE			: Any				   =					"0.12"  # assumed parallelism: 8


if is_torch_available():
 import torch



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=None					)       ->		Optional[int]:
    if rng is None:
        _lowercase			:				List[Any] 						=						random.Random()

    _lowercase			:				Dict 						=						1
    for dim in shape:
        total_dims *= dim

    _lowercase			:				Union[str, Any] 						=						[]
    for _ in range(lowerCamelCase_					):
        values.append(rng.randint(0				,					vocab_size - 1					)					)

    _lowercase			:				Any 						=						np.array(lowerCamelCase_				,					dtype=jnp.intaa					).reshape(lowerCamelCase_					)

    return output



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		str:
    _lowercase			:				Dict 						=						ids_tensor(lowerCamelCase_				,					vocab_size=2				,					rng=lowerCamelCase_					)
    # make sure that at least one token is attended to for each batch
    _lowercase			:				str 						=						1
    return attn_mask






@require_flax
class _lowerCamelCase:
       lowercase_					:      Tuple       					=     None
       lowercase_					:      Tuple       					=     ()
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs_for_common()

           # cut to half length & take max batch_size 3
           _lowercase			:				List[str] 						=						2
           _lowercase			:				int 						=						inputs['input_ids'].shape[-1] // 2
           _lowercase			:				str 						=						inputs['input_ids'][:max_batch_size, :sequence_length]

           _lowercase			:				int 						=						jnp.ones_like(lowerCamelCase)
           _lowercase			:				int 						=						attention_mask[:max_batch_size, :sequence_length]

           # generate max 5 tokens
           _lowercase			:				Union[str, Any] 						=						input_ids.shape[-1] + 5
           if config.eos_token_id is not None and config.pad_token_id is None:
               # hack to allow generate for models such as GPT2 as is done in `generate()`
               _lowercase			:				List[str] 						=						config.eos_token_id
           return config, input_ids, attention_mask, max_length
       @is_pt_flax_cross_test
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Optional[Any] 						=						self._get_input_ids_and_config()
           _lowercase			:				List[str] 						=						False
           _lowercase			:				List[str] 						=						max_length
           _lowercase			:				Optional[Any] 						=						0

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Optional[Any] 						=						model_class(lowerCamelCase)

               _lowercase			:				List[str] 						=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
               _lowercase			:				Tuple 						=						getattr(lowerCamelCase, lowerCamelCase)
               _lowercase			:				Dict 						=						pt_model_class(lowerCamelCase).eval()
               _lowercase			:				Tuple 						=						load_flax_weights_in_pytorch_model(lowerCamelCase, flax_model.params)

               _lowercase			:				Optional[Any] 						=						flax_model.generate(lowerCamelCase).sequences
               _lowercase			:				Optional[Any] 						=						pt_model.generate(torch.tensor(lowerCamelCase, dtype=torch.long))

               if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   _lowercase			:				Dict 						=						flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

               self.assertListEqual(pt_generation_outputs.numpy().tolist(), flax_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						self._get_input_ids_and_config()
           _lowercase			:				Union[str, Any] 						=						False
           _lowercase			:				Tuple 						=						max_length

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Union[str, Any] 						=						model_class(lowerCamelCase)

               _lowercase			:				str 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				Tuple 						=						jit(model.generate)
               _lowercase			:				Union[str, Any] 						=						jit_generate(lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Optional[int] 						=						self._get_input_ids_and_config()
           _lowercase			:				Any 						=						True
           _lowercase			:				Union[str, Any] 						=						max_length

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Any 						=						model_class(lowerCamelCase)

               _lowercase			:				int 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				str 						=						jit(model.generate)
               _lowercase			:				str 						=						jit_generate(lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				int 						=						self._get_input_ids_and_config()
           _lowercase			:				List[str] 						=						False
           _lowercase			:				Optional[Any] 						=						max_length
           _lowercase			:				str 						=						2

           for model_class in self.all_generative_model_classes:
               _lowercase			:				List[str] 						=						model_class(lowerCamelCase)

               _lowercase			:				Dict 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				Optional[Any] 						=						jit(model.generate)
               _lowercase			:				List[str] 						=						jit_generate(lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				List[str] 						=						self._get_input_ids_and_config()
           _lowercase			:				Dict 						=						False
           _lowercase			:				List[Any] 						=						max_length
           _lowercase			:				int 						=						2
           _lowercase			:				Any 						=						2

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Union[str, Any] 						=						model_class(lowerCamelCase)

               _lowercase			:				Dict 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[0], input_ids.shape[0] * config.num_return_sequences)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Optional[int] 						=						self._get_input_ids_and_config()
           _lowercase			:				List[Any] 						=						True
           _lowercase			:				Optional[Any] 						=						max_length
           _lowercase			:				int 						=						0.8
           _lowercase			:				Optional[Any] 						=						10
           _lowercase			:				Dict 						=						0.3
           _lowercase			:				Union[str, Any] 						=						1
           _lowercase			:				Union[str, Any] 						=						8
           _lowercase			:				Optional[Any] 						=						9

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Any 						=						model_class(lowerCamelCase)

               _lowercase			:				str 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				List[str] 						=						jit(model.generate)
               _lowercase			:				List[Any] 						=						jit_generate(lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				List[str] 						=						self._get_input_ids_and_config()
           _lowercase			:				List[str] 						=						max_length
           _lowercase			:				List[Any] 						=						1
           _lowercase			:				Optional[Any] 						=						8
           _lowercase			:				List[Any] 						=						9

           for model_class in self.all_generative_model_classes:
               _lowercase			:				List[str] 						=						model_class(lowerCamelCase)

               _lowercase			:				List[str] 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				Dict 						=						jit(model.generate)
               _lowercase			:				List[Any] 						=						jit_generate(lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						self._get_input_ids_and_config()
           _lowercase			:				Union[str, Any] 						=						max_length
           _lowercase			:				Optional[int] 						=						2
           _lowercase			:				Dict 						=						1
           _lowercase			:				int 						=						8
           _lowercase			:				Optional[int] 						=						9

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Any 						=						model_class(lowerCamelCase)

               _lowercase			:				int 						=						model.generate(lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				Any 						=						jit(model.generate)
               _lowercase			:				str 						=						jit_generate(lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Tuple 						=						self._get_input_ids_and_config()

           # pad attention mask on the left
           _lowercase			:				Any 						=						attention_mask.at[(0, 0)].set(0)

           _lowercase			:				Union[str, Any] 						=						False
           _lowercase			:				Any 						=						max_length

           for model_class in self.all_generative_model_classes:
               _lowercase			:				List[str] 						=						model_class(lowerCamelCase)

               _lowercase			:				Any 						=						model.generate(lowerCamelCase, attention_mask=lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				List[str] 						=						jit(model.generate)
               _lowercase			:				List[str] 						=						jit_generate(lowerCamelCase, attention_mask=lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Optional[int] 						=						self._get_input_ids_and_config()

           # pad attention mask on the left
           _lowercase			:				Optional[int] 						=						attention_mask.at[(0, 0)].set(0)

           _lowercase			:				Tuple 						=						True
           _lowercase			:				int 						=						max_length

           for model_class in self.all_generative_model_classes:
               _lowercase			:				int 						=						model_class(lowerCamelCase)

               _lowercase			:				Optional[int] 						=						model.generate(lowerCamelCase, attention_mask=lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				Optional[Any] 						=						jit(model.generate)
               _lowercase			:				Any 						=						jit_generate(lowerCamelCase, attention_mask=lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())




       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						self._get_input_ids_and_config()

           # pad attention mask on the left
           _lowercase			:				List[str] 						=						attention_mask.at[(0, 0)].set(0)

           _lowercase			:				List[Any] 						=						2
           _lowercase			:				List[Any] 						=						max_length

           for model_class in self.all_generative_model_classes:
               _lowercase			:				Optional[Any] 						=						model_class(lowerCamelCase)

               _lowercase			:				Optional[Any] 						=						model.generate(lowerCamelCase, attention_mask=lowerCamelCase).sequences
               self.assertEqual(generation_outputs.shape[-1], lowerCamelCase)

               _lowercase			:				Tuple 						=						jit(model.generate)
               _lowercase			:				int 						=						jit_generate(lowerCamelCase, attention_mask=lowerCamelCase).sequences

               self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())






@require_flax
class _lowerCamelCase(		unittest.TestCase  ):
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert')
           _lowercase			:				Optional[Any] 						=						FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only')

           _lowercase			:				Optional[int] 						=						'Hello world'
           _lowercase			:				List[Any] 						=						tokenizer(lowerCamelCase, return_tensors='np').input_ids

           # typos are quickly detected (the correct argument is `do_sample`)
           with self.assertRaisesRegex(lowerCamelCase, 'do_samples'):
               model.generate(lowerCamelCase, do_samples=lowerCamelCase)

           # arbitrary arguments that will not be used anywhere are also not accepted
           with self.assertRaisesRegex(lowerCamelCase, 'foo'):
               _lowercase			:				Dict 						=						{'foo': 'bar'}
               model.generate(lowerCamelCase, **lowerCamelCase)





from ..utils import DummyObject, requires_backends







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														requires_backends(self    ,						['''speech''']						)







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														requires_backends(self    ,						['''speech''']						)






